def passed_test(dtype, as_matrix, x_is_row, stride):
"""
Run 2-norm computation test.
Arguments:
dtype: either 'float64' or 'float32', the NumPy dtype to test
as_matrix: True to test a NumPy matrix, False to test a NumPy ndarray
x_is_row: True to test a row vector as parameter x, False to test a column vector
stride: stride of x and y to test; if None, a random stride is assigned
Returns:
True if the expected result is within the margin of error of the actual result,
False otherwise.
"""
# generate random sizes for vector dimensions and vector stride (if necessary)
length = randint(N_MIN, N_MAX)
stride = randint(N_MIN, STRIDE_MAX) if stride is None else stride
# create random vector to test
x = random_vector(length, x_is_row, dtype, as_matrix)
# create view of x that can be used to calculate the expected result
x_2 = x.T if x_is_row else x
# compute the expected result
if stride == 1:
expected = norm(x)
else:
expected = 0
for i in range(0, length, stride):
expected += abs(x_2[i, 0]) ** 2
expected **= 0.5
# get the actual result
actual = nrm2(x, stride)
# compare the actual result to the expected result and return result of the test
return abs(actual - expected) / expected < EPSILON
def test_float64_dtype(self):
x = array([[1., 2., 3., 3., 1., 1.]], dtype='float64')
self.assertEqual(x.dtype, 'float64')
self.assertEqual(nrm2(x), 5)
def test_stride_greater_than_length(self):
x = array([[1., -2., 3., 3., -1., 1.]])
self.assertEqual(nrm2(x, inc_x=6), 1)
def test_stride_less_than_length(self):
x = array([[1., 2., 2., 3., 2., 1.]])
self.assertEqual(nrm2(x, inc_x=2), 3)
def test_vector_as_matrix(self):
x = asmatrix(array([[1.], [2.], [3.], [3.], [1.], [1.]]))
self.assertEqual(nrm2(x), 5)
def test_column_vector_as_ndarray(self):
x = array([[1.], [2.], [3.], [3.], [1.], [1.]])
self.assertEqual(nrm2(x), 5)
def test_negative_element(self):
x = array([[1., -2., 3., 3., -1., 1.]])
self.assertEqual(nrm2(x), 5)
def test_row_vector_as_ndarray(self):
x = array([[1., 2., 3., 3., 1., 1.]])
self.assertEqual(nrm2(x), 5)
def test_scalar_as_ndarray(self):
x = array([[1.]])
self.assertEqual(nrm2(x), 1)
